Next Item

Next-item recommendation aims to predict the item a user will interact with next, a crucial task in personalized systems like e-commerce and streaming services. Current research focuses on improving prediction accuracy using various model architectures, including transformer-based models (like BERT4Rec and SASRec), graph neural networks, and recurrent neural networks (like GRU4Rec), often incorporating user context (e.g., session information, non-item page interactions) and advanced training techniques (e.g., contrastive learning, negative sampling). These advancements enhance recommendation relevance and efficiency, impacting user experience and the effectiveness of personalized marketing strategies.